IST 707 - Applied Machine Learning Welcome to my GitHub repository for IST 707: Applied Machine Learning at Syracuse University. This repository contains all the coursework, including programming assignments, project materials, and additional resources that I have compiled and developed over the course.

Course Overview IST 707 focuses on the application of machine learning techniques and principles to solve real-world data mining problems. The course offers hands-on experience with state-of-the-art software and introduces students to a wide range of data mining methods and tools. The coursework is structured to provide both theoretical and practical knowledge, enabling students to tackle data-driven challenges in various domains.

Instructor Information Professor: Bei Yu ([email protected]) Repository Structure Here’s how this repository is organized:

Assignments/: This directory contains all the homework assignments I completed, showcasing my ability to implement machine learning algorithms and data preprocessing steps. Projects/: Includes the capstone project and other significant projects where I applied comprehensive machine learning pipelines to real datasets.

The topics covered in this course include, but are not limited to:

Data Exploration and Visualization Association Rule Mining Clustering Techniques Classification Algorithms: Decision Trees, Naïve Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests Model Evaluation and Selection Advanced Topics in Machine Learning, including Ensemble Methods and Text Mining

Technologies Used R: Primary programming language used for statistical analysis and machine learning. Weka: Utilized for applying machine learning algorithms without custom code. Additional Tools: Various R packages such as ggplot2, dplyr, caret, and others for data manipulation, visualization, and machine learning.